We propose an interdisciplinary effort linking computational and experimental methods to analyze and model two processes integral to C. elegans physiology: programmed cell death and the regulation of the germline stem cell proliferation decision boundary. The broad aims are to quantify phenotypic variation using image analysis and pattern recognition tools, to use the extracted features and gene expression data to develop a causal model for the regulatory processes, and to validate the model experimentally. The first aim is focused on data collection and the automated scoring of phenotypes in the form of high throughput image acquisition and processing. The scope of the phenotypic data encompasses recombinant progeny from two parental C. elegans strains. The complex regulatory processes under investigation involve cellular-level attributes that are typically assayed via microscopy. We propose pattern recognition algorithms to automate phenotypic scoring. The second aim proposes novel methodology to build integrative models over the joint genotypic, expression and phenotypic datasets. We discuss how the combined genomic data offer immense potential for learning causal models. We propose a learning framework based on a novel modular Bayesian network platform that effectively reduces noise and data complexity. We elaborate on the use of complex optimization techniques designed to avoid local optima in the model scoring procedure. The third aim involves using our models to direct experimentation in efforts to dissect the genetic bases underlying the phenotypes. The causal models provide an explanation of how genetic variations lead to phenotypic change by modulating gene expression. Thus, the causal models can be thought of as biological hypotheses, and promising experimental candidates can be inferred from the models. The proposed research will develop new techniques for synthesizing information from multiple data sources and will integrate these methods with experimental studies of genetic variation in a model research animal, C. elegans. The synergistic interplay of computational methods and experimental analyses will provide a paradigm for greatly accelerating biological discovery. The proposed research is transformative not only in the functionality that it offers to domain scientists but also in the innovative computational research that forms the basis for the work. PUBLIC HEALTH RELEVANCE: We will develop computational models for the analysis of complex biological regulatory processes using recent high-throughput biological data, including measurements of gene expression, parental genotype and physiological traits. Our models are designed to explain how various physiological traits change depending on the state of gene expression and parental genotype. The worm C. elegans offers a rich source of interesting physiological traits that are highly complex in terms of genetics. Therefore, these models will be useful for understanding complex diseases such as cancer.